Reference priors for linear models with general covariance structures

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• 作者：Xin Zhao^a ; ^{xin_zhao@merck.com} ; Martin T. Wells^b
• 关键词：Covariance estimation ; General linear models ; Graphical models ; Linear mixed models ; MCMC ; Penalized splines ; Reference prior ; Smoothing ; Structural equation models
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2012
• 期刊代码：36_03783758
• 类别：mt
• 出版时间：August, 2012
• 卷：142
• 期：8
• 页码：2473-2484
• 文件大小：453 K

摘要

We develop a new class of reference priors for linear models with general covariance structures. A general Markov chain Monte Carlo algorithm is also proposed for implementing the computation. We present several examples to demonstrate the results: Bayesian penalized spline smoothing, a Bayesian approach to bivariate smoothing for a spatial model, and prior specification for structural equation models.